Paper Citation Record · LEDGER

GEM: Empowering LLM for both Embedding Generation and Language Understanding

As of 19 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 2 inbound Pith citation observations for arXiv:2506.04344.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.04344 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:50:51.076830Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T05:40:40.614250Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

34 of 34 outbound references displayed

verified exact0
verified fuzzy3
unresolved31
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 89d5cddc-2109-4a92-8f86-f9af40507ce9 · outbound

This paper cites Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.

GEM: Empowering LLM for both Embedding Generation and Language Understanding Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T10:50:50.446367Z

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Observation 18c2ab01-402e-4006-acbc-11d60474751f · outbound

This paper cites BERT : Pre-training of deep bidirectional transformers for language understanding.

GEM: Empowering LLM for both Embedding Generation and Language Understanding BERT : Pre-training of deep bidirectional transformers for language understanding

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T10:50:50.504529Z

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Observation 385f74ba-f19f-4050-9853-7edde4a801fb · outbound

This paper cites Text and Code Embeddings by Contrastive Pre-Training.

GEM: Empowering LLM for both Embedding Generation and Language Understanding Text and Code Embeddings by Contrastive Pre-Training

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T10:50:50.598694Z

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Observation 1243b383-9268-4dfd-bf42-c37e8c9375b2 · outbound

This paper cites Fine-tuning llama for multi-stage text retrieval.

GEM: Empowering LLM for both Embedding Generation and Language Understanding Fine-tuning llama for multi-stage text retrieval

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:50:51.579902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b5852f53-e774-4fb2-a52e-972368de05d3 · outbound

This paper cites Text Embeddings by Weakly-Supervised Contrastive Pre-training.

GEM: Empowering LLM for both Embedding Generation and Language Understanding Text Embeddings by Weakly-Supervised Contrastive Pre-training

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T10:50:50.820299Z

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Observation 89ee3036-7a6c-4bce-b747-77e10c0636a1 · outbound

This paper cites SimCSE: Simple Contrastive Learning of Sentence Embeddings.

GEM: Empowering LLM for both Embedding Generation and Language Understanding SimCSE: Simple Contrastive Learning of Sentence Embeddings

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T10:50:50.889962Z

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Observation 4ab25975-5191-4e8c-9bd4-41520f22bdd6 · outbound

This paper cites C-pack: Packaged resources to advance general chinese embedding, 2023.

GEM: Empowering LLM for both Embedding Generation and Language Understanding C-pack: Packaged resources to advance general chinese embedding, 2023

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T10:50:50.900593Z

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Observation c5fc8218-d8a9-436b-aaa5-576f9ff075c9 · outbound

This paper cites Repetition Improves Language Model Embeddings.

GEM: Empowering LLM for both Embedding Generation and Language Understanding Repetition Improves Language Model Embeddings

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T10:50:50.906275Z

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Observation 8851ed20-e284-457e-87ac-674e2561cf3d · outbound

This paper cites Generative Representational Instruction Tuning.

GEM: Empowering LLM for both Embedding Generation and Language Understanding Generative Representational Instruction Tuning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T10:50:50.912114Z

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Observation 9700de77-2f49-4fa5-948a-12517ac0b089 · outbound

This paper cites LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders.

GEM: Empowering LLM for both Embedding Generation and Language Understanding LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T10:50:50.918489Z

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Observation ab6a3e69-e4ea-4ebb-8e61-a26ff110038c · outbound

This paper cites MTEB: Massive Text Embedding Benchmark.

GEM: Empowering LLM for both Embedding Generation and Language Understanding MTEB: Massive Text Embedding Benchmark

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T10:50:50.922897Z

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Observation 7525c314-ae57-4354-8548-9a76959bc985 · outbound

This paper cites Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference.

GEM: Empowering LLM for both Embedding Generation and Language Understanding Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T10:50:50.928922Z

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Observation bb5dd380-cf9b-49e1-9c75-b7b0f5df5c91 · outbound

This paper cites Learning to Compress Prompts with Gist Tokens.

GEM: Empowering LLM for both Embedding Generation and Language Understanding Learning to Compress Prompts with Gist Tokens

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T10:50:50.934692Z

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Observation fa0c8107-91a4-4810-9bac-fc901fbcce76 · outbound

This paper cites VoCo-LLaMA: Towards Vision Compression with Large Language Models.

GEM: Empowering LLM for both Embedding Generation and Language Understanding VoCo-LLaMA: Towards Vision Compression with Large Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T10:50:50.940626Z

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Observation 66468508-7e84-4028-9783-158e188347a4 · outbound

This paper cites Generative Pre-trained Transformer: A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directions.

GEM: Empowering LLM for both Embedding Generation and Language Understanding Generative Pre-trained Transformer: A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directions

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T10:50:50.947353Z

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Observation b7a4681f-2b96-4002-8650-670a079aea7e · outbound

This paper cites The Llama 3 Herd of Models.

GEM: Empowering LLM for both Embedding Generation and Language Understanding The Llama 3 Herd of Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T10:50:50.956133Z

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Observation bd1ef6d0-214c-455c-b700-308f01b879d5 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

GEM: Empowering LLM for both Embedding Generation and Language Understanding Gemini: A Family of Highly Capable Multimodal Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T10:50:50.961472Z

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Observation 4308d845-5dff-444b-827f-b9e21b5f35ae · outbound

This paper cites Mistral 7B.

GEM: Empowering LLM for both Embedding Generation and Language Understanding Mistral 7B

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T10:50:50.966606Z

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Observation d5682ada-6695-4761-8aa0-5460b51e9b37 · outbound

This paper cites DeepSeek-V3 Technical Report.

GEM: Empowering LLM for both Embedding Generation and Language Understanding DeepSeek-V3 Technical Report

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T10:50:50.972727Z

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Observation af21dc5a-92e3-42a7-a61c-631129e94067 · outbound

This paper cites Qwen2.5 Technical Report.

GEM: Empowering LLM for both Embedding Generation and Language Understanding Qwen2.5 Technical Report

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T10:50:50.978473Z

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Observation 9e6cca32-97f6-437e-8add-5ff566be37ac · outbound

This paper cites u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \.

GEM: Empowering LLM for both Embedding Generation and Language Understanding u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T10:50:50.983856Z

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Observation 9f93a1f9-d31e-4f71-bd1d-9bb03d683953 · outbound

This paper cites Linformer: Self-Attention with Linear Complexity.

GEM: Empowering LLM for both Embedding Generation and Language Understanding Linformer: Self-Attention with Linear Complexity

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T10:50:50.989466Z

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Observation ccef10ec-d139-45f0-a606-c12549b212c5 · outbound

This paper cites Ring Attention with Blockwise Transformers for Near-Infinite Context.

GEM: Empowering LLM for both Embedding Generation and Language Understanding Ring Attention with Blockwise Transformers for Near-Infinite Context

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T10:50:50.996932Z

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Observation 60657e88-35fb-4aaf-9f6d-3a1bd6e1f174 · outbound

This paper cites Efficient Streaming Language Models with Attention Sinks.

GEM: Empowering LLM for both Embedding Generation and Language Understanding Efficient Streaming Language Models with Attention Sinks

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T10:50:51.002644Z

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Observation a5f70ddc-f502-4087-85e2-06912880a329 · outbound

This paper cites SepLLM: Accelerate Large Language Models by Compressing One Segment into One Separator.

GEM: Empowering LLM for both Embedding Generation and Language Understanding SepLLM: Accelerate Large Language Models by Compressing One Segment into One Separator

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T10:50:51.014693Z

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Observation 35fc2f72-3aad-4cad-8785-1757348e5969 · outbound

This paper cites Adapting Language Models to Compress Contexts.

GEM: Empowering LLM for both Embedding Generation and Language Understanding Adapting Language Models to Compress Contexts

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T10:50:51.020934Z

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Observation 35aed587-e364-4f0c-984e-314615014de2 · outbound

This paper cites A Silver Bullet or a Compromise for Full Attention? A Comprehensive Study of Gist Token-based Context Compression.

GEM: Empowering LLM for both Embedding Generation and Language Understanding A Silver Bullet or a Compromise for Full Attention? A Comprehensive Study of Gist Token-based Context Compression

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T10:50:51.027924Z

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Observation 39adb28a-0505-4479-885e-bb0e6b76633d · outbound

This paper cites In-context Autoencoder for Context Compression in a Large Language Model.

GEM: Empowering LLM for both Embedding Generation and Language Understanding In-context Autoencoder for Context Compression in a Large Language Model

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T10:50:51.035722Z

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Observation 2a349dfa-8ce7-4ec9-9dcc-80c8a642da10 · outbound

This paper cites Simple and Scalable Strategies to Continually Pre-train Large Language Models.

GEM: Empowering LLM for both Embedding Generation and Language Understanding Simple and Scalable Strategies to Continually Pre-train Large Language Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T10:50:51.041868Z

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Observation a4065ce3-fe3c-4bac-b82d-d8b8dcc93864 · outbound

This paper cites Dense Passage Retrieval for Open-Domain Question Answering.

GEM: Empowering LLM for both Embedding Generation and Language Understanding Dense Passage Retrieval for Open-Domain Question Answering

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T10:50:51.049808Z

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Observation 1b73f1a8-f33d-4d48-b8e1-7e3cd0727a6a · outbound

This paper cites Learning Transferable Visual Models From Natural Language Supervision.

GEM: Empowering LLM for both Embedding Generation and Language Understanding Learning Transferable Visual Models From Natural Language Supervision

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T10:50:51.057601Z

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Observation 1cc475ff-34c6-427c-bfe6-37fa697217e8 · outbound

This paper cites Aligning ai with shared human values.

GEM: Empowering LLM for both Embedding Generation and Language Understanding Aligning ai with shared human values

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:50:51.542254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f000a97b-4201-4e5f-930a-c52f567bd2a3 · outbound

This paper cites Measuring massive multitask language understanding.

GEM: Empowering LLM for both Embedding Generation and Language Understanding Measuring massive multitask language understanding

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:50:51.524730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 340dc06a-e7c4-4e88-9387-69b785d2b744 · outbound

This paper cites Efficient Continual Pre-training by Mitigating the Stability Gap.

GEM: Empowering LLM for both Embedding Generation and Language Understanding Efficient Continual Pre-training by Mitigating the Stability Gap

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T10:50:51.076830Z

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Pith citing papers

Observation 56b8f86f-b531-47e5-9b96-810e811239b1 · inbound

HT-Transformer: Event Sequences Classification by Accumulating Prefix Information with History Tokens cites this paper.

HT-Transformer: Event Sequences Classification by Accumulating Prefix Information with History Tokens GEM: Empowering LLM for both Embedding Generation and Language Understanding

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T05:40:40.614250Z

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Observation 0437573e-5622-4973-baf6-20be6a77b38d · inbound

A Unified Model and Document Representation for On-Device Retrieval-Augmented Generation cites this paper.

A Unified Model and Document Representation for On-Device Retrieval-Augmented Generation GEM: Empowering LLM for both Embedding Generation and Language Understanding

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-10T11:55:20.131760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.